def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
data_loader = get_data_loader(cfg)
spec_loss = SpectralCLusterLayer()
model = Model(cfg.vector_size, cfg.fix_weight)
model_w = DataParallel(model)
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr = cfg.lr)
modules_optims = [model, optimizer]
TMO(modules_optims)
may_set_mode(modules_optims, 'train')
for epoch in range(cfg.total_epoch):
epoch_done = False
step = 0
while not epoch_done:
step += 1
ims, _, labels, epoch_done= data_loader.next_batch()
ims_var = Variable(TVT(torch.from_numpy(ims).float()))
batch_size = ims_var.size()[0]
num_cluster = len(data_loader.ids)
labels_matrix = np.zeros([batch_size, num_cluster], dtype=int)
labels_matrix[range(batch_size), labels-1] = 1
labels_var = TVT(torch.from_numpy(labels_matrix).float())
optimizer.zero_grad()
feat = model_w(ims_var)
G = spec_loss.grad_F(feat, labels_var)
feat.backward(gradient=G)
optimizer.step()
objective_value = labels_var.size()[1] - torch.sum(torch.mm(spec_loss.pseudo_inverse(labels_var),feat) * torch.mm(spec_loss.pseudo_inverse(feat), labels_var).t())
print("epoch %d --- loss value= %f" % (epoch, objective_value))
print "Finished"
def logreg_train(args):
d = load_file(args.dataset)
try:
# Sanitize dataset
d = d.dropna(subset=['Herbology', 'Ancient Runes', 'Astronomy'])
X = np.array(d.values[:, [8, 12, 7]], dtype=float)
y = d.values[:, 1]
# Init model
if args.stochastic:
args.batch = 1
model = Model(args.iter, args.learning, int(args.batch) > 0, args.batch, args.precision, args.visualizer)
# Normalize features
X = np.array([normalize(t) for t in X.T]).T
new_df = pd.DataFrame(X)
# Convert guild names to integers indexes)
Y = []
for i in y:
Y.append(model.feature_i[i])
y = np.array(Y, dtype=int)
y_unique = np.unique(y)
# Execute logistic regression
model.process_logreg(X, y)
except Exception as e:
print ("error : {0}".format(e))
def logreg_predict(args):
d = load_file(args.dataset)
v = load_file(args.values)
try:
# Sanitize dataset
d = d.fillna(0)
# Normalize features
X = np.array(d.values[:, [8, 12, 7]], dtype=float)
X = np.array([normalize(t) for t in X.T]).T
X = np.insert(X, 0, 1, axis=1)
theta = np.array(v.values[:, 1:].T, dtype=float)
model = Model()
prediction = model.hypothesis(theta, X)
# Convert integers indexes to guild names)
houses = np.argmax(prediction, axis=1)
matching_houses = list(map(lambda v: model.i_feature[v], houses))
write_prediction(matching_houses)
print("houses.csv successfully written !")
if args.show:
greek_god_graph(matching_houses, X, model)
except Exception as e:
print("error : {0}".format(e))
sys.exit(-1)
def getting_model(self, args):
print('getting model...')
if args.train or args.test:
self.model = Model(batch_size = self.batch_size , val_size = self.val_size,
max_len = self.max_len ,args = args , dictionary = self.dictionary)#
self.model.compile()
if (args.train and args.model_restore) or args.test:
self.model.restore(mode = self.mode)
def init_db_models():
with open(INIT_MODEL_DATA, 'r') as csvfile:
model_list = csv.reader(csvfile, delimiter=',', quotechar='"')
next(model_list, None) # Skip header line
for row in model_list:
brand = Brand.query.get(row[1])
model = Model(id=row[0], name=row[2], brand=brand)
db.session.add(model)
db.session.commit()
def updateModel(model_id):
if not request.json or not 'name' in request.json or not 'id' in request.json:
return jsonify({"result": False, "msg": "Failed to Update Model!"})
model = Model(request.json['name'], request.json['dataset'])
result = Model.updateModel(model, mysql)
if result is True:
return jsonify({"result": True, "msg": "Successfully Updated Model!"})
return jsonify({"result": False, "msg": "Failed to Update Model!"})
def processUserResponse(update, context, user_msg):
REPLY_MARKUP = telegram.ReplyKeyboardRemove()
chat_id = update.effective_chat.id
# Initiate new user object
if chat_id not in ACTIVE_USERS:
ACTIVE_USERS[chat_id] = User(chat_id, update.effective_chat.first_name)
# Set user language
if chat_id in ACTIVE_USERS.keys() and user_msg in config.sections(
) and ACTIVE_USERS[chat_id].getLang() == "DEFAULT":
ACTIVE_USERS[chat_id].setLang(user_msg)
# Set chat language
user_lang = ACTIVE_USERS[chat_id].getLang()
if user_lang == "DEFAULT" and len(config.sections()) > 1:
REPLY_MARKUP = lang_reply_markup
print(config.sections())
MESSAGE = config['DEFAULT']['LANG_MESSAGE']
REPLY_MARKUP = lang_reply_markup
# If User questionary is already in process then process user response:
elif chat_id in ACTIVE_USERS.keys() and ACTIVE_USERS[chat_id].isModel():
MESSAGE = ACTIVE_USERS[chat_id].getModel().processQuestion(user_msg)
REPLY_MARKUP = ACTIVE_USERS[chat_id].getModel().getMarkup()
# If last question in the questionary:
if ACTIVE_USERS[chat_id].getModel().getStatus() == 0:
saveAnswers(update, context,
ACTIVE_USERS[chat_id].getModel().getAnswers())
ACTIVE_USERS[chat_id].setModel("NA")
MESSAGE += config[user_lang]['BYE_MESSAGE']
# If User is in list, but have not started questionary:
# Initialize Questionary
elif chat_id in ACTIVE_USERS.keys(
) and user_msg in config[user_lang]['categories'].split(","):
ACTIVE_USERS[chat_id].setModel(
Model(model_name=config[user_lang]['models'].split(",")[
config[user_lang]['categories'].split(",").index(user_msg)],
user_lang=user_lang))
MESSAGE = ACTIVE_USERS[chat_id].getModel().processQuestion(user_msg)
# Init new user
# Show greeting message one more time
else:
REPLY_MARKUP = start_reply_markup(user_lang)
MESSAGE=config[user_lang]['GREETING_WORD']+" " \
+ACTIVE_USERS[chat_id].getName()+"! "+config[user_lang]['WELCOME_MESSAGE']
context.bot.send_message(chat_id=chat_id,
text=MESSAGE,
parse_mode=telegram.ParseMode.HTML,
reply_markup=REPLY_MARKUP)
def get_model(input_channels,
input_time_length,
dilations=None,
kernel_sizes=None,
padding=False):
"""
initializes a new Deep4Net and changes the kernel sizes and dilations of the network based on the input parameters
:param input_channels: 1 axis input shape
:param input_time_length: 0 axis input shape
:param dilations: dilations of the max-pool layers of the network
:param kernel_sizes: kernel sizes of the max-pool layers of the network
:param padding: if padding is to be added
:return: a Model object, the changed Deep4Net based on the kernel sizes and dilation parameters and the name
of the model based on the kernel sizes and dilatiosn
"""
if kernel_sizes is None:
kernel_sizes = [3, 3, 3, 3]
print('SBP False!!!')
model = Model(input_channels=input_channels,
n_classes=1,
input_time_length=input_time_length,
final_conv_length=2,
stride_before_pool=False)
model.make_regressor()
if cuda:
model.model = model.model.cuda()
model_name = get_model_name_from_kernel_and_dilation(
kernel_sizes, dilations)
changed_model = change_network_kernel_and_dilation(model.model,
kernel_sizes,
dilations,
remove_maxpool=False)
# print(changed_model)
return model, changed_model, model_name
def factory_model():
if Model.counter == 0:
return Model()
def initialize_model(global_path, image_size, image_format, config, loss_type):
model = None
torch.cuda.empty_cache()
gc.collect()
epochs, lr, leaky_thresh, lamda, beta1, beta2 = get_model_params(config)
if loss_type == 'hybrid_l1':
model = Hybrid_L1_Model(base_path=global_path,
image_size=image_size,
image_format=image_format,
epochs=epochs,
learning_rate=lr,
leaky_relu=leaky_thresh,
lamda=lamda,
betas=(beta1, beta2))
elif loss_type == 'hybrid_l2':
model = Hybrid_L2_Model(base_path=global_path,
image_size=image_size,
image_format=image_format,
epochs=epochs,
learning_rate=lr,
leaky_relu=leaky_thresh,
lamda=lamda,
betas=(beta1, beta2))
elif loss_type == 'l1':
model = L1_Model(base_path=global_path,
image_size=image_size,
image_format=image_format,
epochs=epochs,
learning_rate=lr,
leaky_relu=leaky_thresh,
lamda=lamda,
betas=(beta1, beta2))
elif loss_type == 'l2':
model = L2_Model(base_path=global_path,
image_size=image_size,
image_format=image_format,
epochs=epochs,
learning_rate=lr,
leaky_relu=leaky_thresh,
lamda=lamda,
betas=(beta1, beta2))
elif loss_type == 'perpetual':
model = Perpetual_Model(base_path=global_path,
image_size=image_size,
image_format=image_format,
epochs=epochs,
learning_rate=lr,
leaky_relu=leaky_thresh,
lamda=lamda,
betas=(beta1, beta2))
elif loss_type == 'default':
model = Model(base_path=global_path,
image_size=image_size,
image_format=image_format,
epochs=epochs,
learning_rate=lr,
leaky_relu=leaky_thresh,
lamda=lamda,
betas=(beta1, beta2))
else:
raise NotImplementedError(
'This Loss function has not been implemented!')
average_loss = AverageLoss(os.path.join(global_path, 'Loss_Checkpoints'))
return model, average_loss
import sys
from utils import readjson
from models.SimpleRergression import Linear
from utils.DataLoader import DataLoader
from models.Model import Model
if __name__ == '__main__':
config = readjson(sys.argv[1])
linear = Linear(**config['linear'])
dataloader = DataLoader(**config['dataloader'])
modal = Model(linear, dataloader, **config['modal'])
modal.fit()
def __init__(self):
self._close_funcs = []
self.model = Model()
self.fig_widget = None
self.main_view = MainView(self)
self.main_view.show()
# creates flask apirest full
app = Flask(__name__, template_folder='views')
api = Api(app)
# configures mongodb connectrion string
# app.config["MONGO_URI"] = "mongodb://{}:{}@{}:{}/{}".format(cfg.username,
# cfg.password,
# cfg.host,
# cfg.port,
# cfg.dbname)
app.config["MONGO_URI"] = "mongodb://{}:{}/{}".format(cfg.host, cfg.port,
cfg.dbname)
# creates the model
model = Model(PyMongo(app))
# register the api routes and controllers
api.add_resource(ControllerHome, '/', resource_class_kwargs={'model': model})
api.add_resource(ControllerSats,
'/sats',
resource_class_kwargs={
'model': model,
'publicKey': secretKey['public']
})
api.add_resource(ControllerToken,
'/token/<int:id>/<int:minutes>/<token>',
resource_class_kwargs={
'model': model,
# 各クラステスト実行用のクラス
from models.Model import Model
from NiceBoatUtils import RaceUtil
import boatticket as bt
import numpy as np
ml = Model()
pred = ml.predict(0)
print("predict race time")
print(pred)
print("win rate")
win_rate = RaceUtil.win_rate(pred)
print(win_rate)
ex = bt.exacta.Exacta(win_rate)
print("2連単")
print(ex.predict())
qu = bt.quinella.Quinella(win_rate)
print("2連複")
print(qu.predict())
tri = bt.trio.Trio(win_rate)
print("3連複")
print(tri.predict())
"""
Tested on python version : 3.5.2
"""
# parameters
params = {
"max_epoch": 60,
"learning_rate": 0.001,
"batch_size": 8,
"post_padding_size": 10,
"comment_padding_size": 20,
"n_hidden": 100,
"num_filters": 150,
"filter_sizes": [3, 4, 5],
"keep_prob_global_train": 0.6,
"bidirectional": False,
"binary_sentiment": False,
"display_step": 100,
"evaluate_every": 1,
# Constants
"word2vec_dim": 300,
"n_classes_topics": len(Resources.topics()),
"n_classes_emotion": len(Resources.emotions()),
"n_classes_speech_acts": len(Resources.speech_acts()),
}
params["n_classes_sentiment"] = len(Resources.binary_sentiment(
)) if params["binary_sentiment"] is True else len(Resources.sentiment())
model = Model(params)
model.start()
def __init__(self, sys_argv):
super(App, self).__init__(sys_argv)
self.model = Model()
# listView = QtGui.QListView()
# listView.show
red = QtGui.QColor(255, 0, 0)
green = QtGui.QColor(0, 255, 0)
blue = QtGui.QColor(0, 0, 255)
rowCount = 4
columnCount = 2
tableData1 = [[QtGui.QColor("#FFFF00") for i in range(columnCount)]
for j in range(rowCount)]
headers = ["Pallet0", "Colors"]
entity = json2obj(
'{"category":"groups","path":"/mnt/x19/mavisdev/projects/geotest/sequence/afg_0025","name":"afg_0025","description":"AFG_0025 sequence","fileImportPath":"","isGlobal":false,"project":"geotest","fields":{"priority":"medium","status":"idle"},"createdBy":"trevor","createdAt":"2016-09-13T20:28:04.745Z","updatedAt":"2017-05-31T21:38:19.935Z","id":"57d861546fef3a0001c87954","type":"sequence","mediaIds":[],"isTest":false}'
)
entity1 = json2obj(
'{"category":"assets","path":"/mnt/x19/mavisdev/projects/geotest/globals/assets/wood_log","name":"wood_log","description":"a log that is wooden","fileImportPath":"","isGlobal":false,"project":"geotest","fields":{"priority":"medium","status":"review","grouping":"char","comp_status":"Ready","prod_status":"HIGH"},"createdBy":"dexplorer","createdAt":"2017-06-12T20:07:21.739Z","updatedAt":"2017-06-12T20:07:21.798Z","id":"593ef47973d9f40001cf898b","type":"assets","mediaIds":[],"isTest":false}'
)
entity2 = json2obj(
'{"category":"assets","path":"/mnt/x19/mavisdev/projects/geotest/sequence/afg_0025/shots/afg_0025_0020/plates/plate_afg-0025__0020","name":"plate_afg-0025__0020","description":"plate asse for afg_0025_0020","latest":"583dc9eebc843d0001905bde","fileImportPath":"/mnt/x1/mavisdev/client_imports/geotest/afg_0025_0020/AFG_0025_0020_bg01_v001_LIN.exr","isGlobal":true,"project":"geotest","fields":{"priority":"low","status":"approved","startFrame":10,"endFrame":100,"pxAspect":1,"colorspace":"linear","fileType":"exr","width":1920,"height":1080,"lut":"","ccc":"","head":8,"tail":8,"handle":8},"createdBy":"trevor","createdAt":"2016-11-29T18:31:59.429Z","updatedAt":"2017-05-23T21:17:43.390Z","id":"583dc99fbc843d0001905bd9","type":"plates","mediaIds":[],"parentId":"57d861546fef3a0001c87960","isTest":false}'
)
entity3 = json2obj(
'{"category":"tasks","path":"/mnt/x19/mavisdev/projects/geotest/globals/assets/wood_log/texture/tex_log","name":"tex_log","description":"texture the wood log","latest":"5941b18073d9f40001cf8a6c","fileImportPath":"","isGlobal":false,"project":"geotest","fields":{"priority":"urgent","status":"revised","grouping":"mtpg","comp_status":"In-Progress","prod_status":"HIGH"},"createdBy":"dexplorer","createdAt":"2017-06-12T20:08:10.814Z","updatedAt":"2017-06-14T21:58:24.772Z","id":"593ef4aa73d9f40001cf8992","type":"texture","mediaIds":[],"isTest":false}'
)
entity4 = json2obj(
'{"category":"tasks","path":"/mnt/x19/mavisdev/projects/geotest/sequence/mdm_0202/shots/mdm_0202_0100/assets/tuktuka/model/tuktuk_model","name":"tuktuk_model","description":"published plate 6310","latest":"58c6ffe6e925cc00016a6b58","fileImportPath":"","isGlobal":false,"project":"geotest","fields":{"priority":"high","status":"revised","grouping":"vehi","comp_status":"Waiting","prod_status":"MEDIUM"},"createdBy":"trevor","createdAt":"2017-04-13T22:08:33.983Z","updatedAt":"2017-04-18T20:35:28.557Z","id":"589b4f9dc599d10001375de9","type":"model","mediaIds":[],"parentId":"589b4f10c599d10001375de2","isTest":false}'
)
rootNode = Node('Hips')
childNode0 = TransformNode('LeftPirateleg', entity, rootNode)
childNode1 = Node('RightLeg', entity1, rootNode)
childNode2 = Node('RightFoot', entity2, childNode1)
childNode3 = CameraNode('Xxxree', entity3, rootNode)
childNode4 = LightNode('kldjskfds', entity4, childNode1)
tree = TreeModel(rootNode)
model2 = PaletteTableModel(tableData1, headers)
self.main_ctrl = MainController(self.model)
self.main_view = MainView(model=self.model, main_ctrl=self.main_ctrl)
self.main_view.test(model2, tree=tree)
self.main_view.show()
# model2.insertRows(0, 5)
# model2.insertColumns(0, 5)
model2.removeColumns(1, 1)
# tree.insertRows(0, 1)
#
#
# self.threadClass = ThreadClass()
# self.connect(self.threadClass, QtCore.SIGNAL('CPU_VALUE'), self.done)
# self.threadClass.start()
self.manager = QtNetwork.QNetworkAccessManager()
self.manager.finished.connect(self.reply_finished)
print(
QtNetwork.QNetworkSession(QtNetwork.QNetworkConfigurationManager().
defaultConfiguration()).State())
self.request = QtNetwork.QNetworkRequest(
QtCore.QUrl(
'http://www.planwallpaper.com/static/images/1080p-HD-Wallpapers-9.jpg'
))
print("Sending request")
self.manager.get(self.request)
self.manager2 = QtNetwork.QNetworkAccessManager()
self.manager2.finished.connect(self.reply_finished)
print(
QtNetwork.QNetworkSession(QtNetwork.QNetworkConfigurationManager().
defaultConfiguration()).State())
self.request = QtNetwork.QNetworkRequest(
QtCore.QUrl('http://lorempixel.com/1800/1400/city/'))
print("Sending request")
self.manager2.get(self.request)
self.manager2 = QtNetwork.QNetworkAccessManager()
self.manager2.finished.connect(self.reply_finished)
print(
QtNetwork.QNetworkSession(QtNetwork.QNetworkConfigurationManager().
defaultConfiguration()).State())
self.request = QtNetwork.QNetworkRequest(
QtCore.QUrl('http://lorempixel.com/1800/1400/city/'))
print("Sending request")
self.manager2.get(self.request)
self.manager3 = QtNetwork.QNetworkAccessManager()
self.manager3.finished.connect(self.reply_finished)
print(
QtNetwork.QNetworkSession(QtNetwork.QNetworkConfigurationManager().
defaultConfiguration()).State())
self.request = QtNetwork.QNetworkRequest(
QtCore.QUrl('http://lorempixel.com/1800/1400/city/'))
print("Sending request")
self.manager3.get(self.request)
